Analyses of large ensemble data on future climate are significantly useful for the probabilistic future projection of climate change in various interdisciplinary fields. However, the data volume of the Database for Policy Decision making for Future climate change or d4PDF, which is a mega-ensemble dataset, exceeds ∼ 3 PB, which is too large to download to local computers. To allow users for retrieve and downloading necessary data, we developed a user-friendly system called “System for Efficient content-based retrieval to Analyze Large volume climate data” (SEAL) under the Social Implementation Program on Climate Change Adaptation Technology (SI-CAT). Conventional web-based retrieval systems allow retrievals using metadata associated with a data file itself. In contrast, SEAL allows the users to retrieve the necessary data by using metadata associated with contents, such as physical values, of a data file. We confirmed that SEAL can reduce data sizes and total time required for obtaining necessary data to less than 0.5% and 1%, respectively, compared to conventional web-based retrieval systems.

中文翻译：

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开发用于基于内容的有效检索以分析大量气候数据的系统

有关未来气候的大型总体数据的分析对于跨学科领域中气候变化的概率未来预测非常有用。但是，作为一个巨大的整体数据集，“未来气候变化政策决策数据库”或d4PDF的数据量超过3 PB，太大，无法下载到本地计算机。为了允许用户检索和下载必要的数据，我们在气候变化适应技术社会实施计划（SI-CAT）下开发了一个用户友好的系统，该系统称为“基于内容的高效检索系统，以分析大量气候数据”（SEAL）。 ）。常规的基于Web的检索系统允许使用与数据文件本身关联的元数据进行检索。相反，SEAL允许用户通过使用与数据文件的内容（例如，物理值）相关联的元数据来检索必要的数据。我们确认，与传统的基于Web的检索系统相比，SEAL可以将获得必要数据所需的数据大小和总时间分别减少到不足0.5％和1％。